Synaptic vesicle fusion is modulated through feedback inhibition by dopamine auto‐receptors
Bibliographic record
Abstract
Mechanisms of synaptic vesicular fusion and neurotransmitter clearance are highly controlled processes whose finely-tuned regulation is critical for neural function. This modulation has been suggested to involve pre-synaptic auto-receptors; however, their underlying mechanisms of action remain unclear. Previous studies with the well-defined C. elegans nervous system have used functional imaging to implicate acid sensing ion channels (ASIC-1) to describe synaptic vesicle fusion dynamics within its eight dopaminergic neurons. Implementing a similar imaging approach with a pH-sensitive fluorescent reporter and fluorescence resonance after photobleaching (FRAP), we analyzed dynamic imaging data collected from individual synaptic termini in live animals. We present evidence that constitutive fusion of neurotransmitter vesicles on dopaminergic synaptic termini is modulated through DOP-2 auto-receptors via a negative feedback loop. Integrating our previous results showing the role of ASIC-1 in a positive feedback loop, we also put forth an updated model for synaptic vesicle fusion in which, along with DAT-1 and ASIC-1, the dopamine auto-receptor DOP-2 lies at a modulatory hub at dopaminergic synapses. Our findings are of potential broader significance as similar mechanisms are likely to be used by auto-receptors for other small molecule neurotransmitters across species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".